



'''simple docstring'''

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__	     =0






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =Path(_UpperCAmelCase						) / 'preprocessor_config.json'
							snake_case__	     =Path(_UpperCAmelCase						) / 'config.json'
							json.dump(
							    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}					,   open(_UpperCAmelCase					,   'w'						)					,   )
							json.dump({'model_type': 'clip'}					,   open(_UpperCAmelCase					,   'w'						)						)

							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =Path(_UpperCAmelCase						) / 'preprocessor_config.json'
							snake_case__	     =Path(_UpperCAmelCase						) / 'config.json'
							json.dump(
							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}					,   open(_UpperCAmelCase					,   'w'						)					,   )
							json.dump({'model_type': 'clip'}					,   open(_UpperCAmelCase					,   'w'						)						)

							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =CLIPConfig()

							# Create a dummy config file with image_proceesor_type
							snake_case__	     =Path(_UpperCAmelCase						) / 'preprocessor_config.json'
							snake_case__	     =Path(_UpperCAmelCase						) / 'config.json'
							json.dump(
							    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}					,   open(_UpperCAmelCase					,   'w'						)					,   )
							json.dump({'model_type': 'clip'}					,   open(_UpperCAmelCase					,   'w'						)						)

							# remove image_processor_type to make sure config.json alone is enough to load image processor locally
							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase						).to_dict()

							config_dict.pop('image_processor_type'						)
							snake_case__	     =CLIPImageProcessor(**_UpperCAmelCase						)

							# save in new folder
							model_config.save_pretrained(_UpperCAmelCase						)
							config.save_pretrained(_UpperCAmelCase						)

							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase						)

							# make sure private variable is not incorrectly saved
							snake_case__	     =json.loads(config.to_json_string()						)
							self.assertTrue('_processor_class' not in dict_as_saved						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							str:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =Path(_UpperCAmelCase						) / 'preprocessor_config.json'
							json.dump(
							    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}					,   open(_UpperCAmelCase					,   'w'						)					,   )

							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase						)
							self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					with self.assertRaisesRegex(
					    _UpperCAmelCase					,   'clip-base is not a local folder and is not a valid model identifier'						):
							snake_case__	     =AutoImageProcessor.from_pretrained('clip-base'						)






			def 						_lowercase  (     self						)							->							List[str]:
					with self.assertRaisesRegex(
					    _UpperCAmelCase					,   r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase					,   revision='aaaaaa'						)






			def 						_lowercase  (     self						)							->							List[str]:
					with self.assertRaisesRegex(
					    _UpperCAmelCase					,   'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'					,   ):
							snake_case__	     =AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'						)






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_UpperCAmelCase						):
							snake_case__	     =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_UpperCAmelCase						):
							snake_case__	     =AutoImageProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_image_processor'					,   trust_remote_code=_UpperCAmelCase						)

					snake_case__	     =AutoImageProcessor.from_pretrained(
					    'hf-internal-testing/test_dynamic_image_processor'					,   trust_remote_code=_UpperCAmelCase						)
					self.assertEqual(image_processor.__class__.__name__					,   'NewImageProcessor'						)

					# Test image processor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(_UpperCAmelCase						)
							snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase					,   trust_remote_code=_UpperCAmelCase						)
					self.assertEqual(reloaded_image_processor.__class__.__name__					,   'NewImageProcessor'						)






			def 						_lowercase  (     self						)							->							Any:
					try:
							AutoConfig.register('custom'					,   _UpperCAmelCase						)
							AutoImageProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(_UpperCAmelCase						):
									AutoImageProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)

							with tempfile.TemporaryDirectory() as tmpdirname:
									snake_case__	     =Path(_UpperCAmelCase						) / 'preprocessor_config.json'
									snake_case__	     =Path(_UpperCAmelCase						) / 'config.json'
									json.dump(
									    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}					,   open(_UpperCAmelCase					,   'w'						)					,   )
									json.dump({'model_type': 'clip'}					,   open(_UpperCAmelCase					,   'w'						)						)

									snake_case__	     =CustomImageProcessor.from_pretrained(_UpperCAmelCase						)

							# Now that the config is registered, it can be used as any other config with the auto-API
							with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(_UpperCAmelCase						)
									snake_case__	     =AutoImageProcessor.from_pretrained(_UpperCAmelCase						)
									self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
									del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






			def 						_lowercase  (     self						)							->							Optional[Any]:

					class 							a__( snake_case__     ):
								a_	: int									=      True

					try:
							AutoConfig.register('custom'					,   _UpperCAmelCase						)
							AutoImageProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							# If remote code is not set, the default is to use local
							snake_case__	     =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
							self.assertEqual(image_processor.__class__.__name__					,   'NewImageProcessor'						)
							self.assertTrue(image_processor.is_local						)

							# If remote code is disabled, we load the local one.
							snake_case__	     =AutoImageProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_image_processor'					,   trust_remote_code=_UpperCAmelCase						)
							self.assertEqual(image_processor.__class__.__name__					,   'NewImageProcessor'						)
							self.assertTrue(image_processor.is_local						)

							# If remote is enabled, we load from the Hub
							snake_case__	     =AutoImageProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_image_processor'					,   trust_remote_code=_UpperCAmelCase						)
							self.assertEqual(image_processor.__class__.__name__					,   'NewImageProcessor'						)
							self.assertTrue(not hasattr(_UpperCAmelCase					,   'is_local'						)						)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
									del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]









'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




